Unsupervised Discovery of Interpretable Directions in the GAN Latent Space (2002.03754v3)

Abstract: The latent spaces of GAN models often have semantically meaningful directions. Moving in these directions corresponds to human-interpretable image transformations, such as zooming or recoloring, enabling a more controllable generation process. However, the discovery of such directions is currently performed in a supervised manner, requiring human labels, pretrained models, or some form of self-supervision. These requirements severely restrict a range of directions existing approaches can discover. In this paper, we introduce an unsupervised method to identify interpretable directions in the latent space of a pretrained GAN model. By a simple model-agnostic procedure, we find directions corresponding to sensible semantic manipulations without any form of (self-)supervision. Furthermore, we reveal several non-trivial findings, which would be difficult to obtain by existing methods, e.g., a direction corresponding to background removal. As an immediate practical benefit of our work, we show how to exploit this finding to achieve competitive performance for weakly-supervised saliency detection.

Unsupervised Discovery of Interpretable Directions in the GAN Latent Space

The paper "Unsupervised Discovery of Interpretable Directions in the GAN Latent Space" by Andrey Voynov and Artem Babenko presents a novel approach to discovering interpretable directions in the latent space of Generative Adversarial Networks (GANs) without supervision. This research addresses a critical aspect of GANs, which are widely used across various applications in the computer vision domain, such as image editing and video generation.

Overview

GANs have shown remarkable ability in generating high-resolution, realistic images. However, utilizing GANs in a controllable manner necessitates understanding the semantic structure of their latent spaces. Previous attempts to discover interpretable directions in these spaces have relied on supervisory signals like human labels or pretrained models. Such methods limit the scope of discoverable directions due to dependency on labeled data or specific pretrained networks.

This paper shifts the paradigm by introducing an unsupervised method that identifies semantically meaningful directions in the GAN latent space. The method depends on a model-agnostic procedure which does not require retraining of the generator or access to human labels. By optimizing a matrix of directions alongside a reconstructor network that distinguishes image transformations induced by these directions, the approach isolates factors of variation in an unsupervised manner.

Key Contributions

1. Unsupervised Methodology: The authors propose the first unsupervised framework to find interpretable directions in the latent space, applicable across different GAN architectures without retraining. This approach broadens the possibilities for GAN interpretability beyond the constraints of supervised methods.
2. Practical Discoveries: The approach unveiled directions that relate to non-trivial transformations, such as background removal, which existing methods could not attain without supervision. Such findings offer practical utility, for instance, in generating synthetic data for computer vision tasks like weakly-supervised saliency detection.
3. Competitive Performance in Saliency Detection: By exploiting the discovered background removal direction, the method successfully synthesized data for saliency detection, achieving competitive results—demonstrating immediate practical applicability.

Implications and Future Developments

The implications of this work span both practical applications and theoretical understanding. Practically, the method paves the way for more accessible and extensive applications of GANs in scenarios requiring interpretability and controllable outputs, such as image editing and enhancement. Theoretically, it challenges the notion that supervision is necessary for understanding GAN latent spaces, opening new research avenues into unsupervised learning and representation disentanglement.

Future developments may involve extending the unsupervised methodology to other generative models beyond GANs and exploring mechanisms to automatically assess and quantify the interpretability and separability of discovered directions across more complex and varied datasets.

Conclusion

The paper makes significant headway in the interpretability of GANs by introducing an unsupervised strategy to discover meaningful latent directions. This work contributes to the broader understanding of generative models and enhances the utility of GANs in practical applications by enabling controlled manipulations without the need for expensive supervision. This advancement is likely to inspire subsequent research efforts in unveiling implicit structures in the latent spaces of not only GANs but also other generative modeling frameworks.